Analyzing the Role of Community and Individual Factors in LAMP Grant Funding: Identifying Potential Barriers Across US Counties
Executive Summary
This report investigates community characteristics influencing the allocation of USDA’s Local Agriculture Market Program (LAMP) grant funding. LAMP supports local food systems through grants to promote producer-to-consumer marketing, food enterprises, and value-added products. The analysis includes data on socioeconomic and environmental factors, using Principal Component Analysis (PCA), clustering, and regression analysis to identify patterns and key variables affecting grant distribution. Key findings highlight that educational levels and infrastructure significantly impact funding success. Recommendations include expanding data sources, conducting longitudinal studies, and enhancing policy evaluations to ensure equitable access to LAMP resources.
Introduction
Local Agriculture Market Program (LAMP)
The USDA’s Agricultural Marketing Service (AMS) administers a variety of grant programs aimed at strengthening local and regional food systems. The Local Agriculture Market Program (LAMP) is one such program that supports direct producer-to-consumer marketing, food enterprises, and value-added agricultural products. Established under the 2018 Farm Bill, LAMP fosters community collaboration and public-private partnerships to improve regional food economies, aiding in the development of business strategies and infrastructure for local food systems. The Farm Bill provided LAMP $50 million per year in mandatory funding and the programs received significant supplemental funding through the Consolidated Appropriations Act of 2021 and the American Rescue Plan of 2021.1 The major grant programs within LAMP include the Local Food Promotion Program (LFPP),2 Regional Food Systems Partnership (RFSP),3 and the Farmers Market Promotion Program (FMPP).4
Building community capital through food systems investment
The goals of the LAMP program include: simplifying the application processes and the reporting processes for the Program; improving income and economic opportunities for producers and food businesses through job creation; and strengthening capacity and regional food system development through community collaboration and expansion of mid-tier value chains.5
USDA aims to make LAMP funding available to all eligible entities, with a particular emphasis on underserved communities. While the extent to which these aims are being met is not entire clear, AMS is working to better understand the impacts of these efforts. In 2021, for example, AMS partnered with Florida A&M University and the University of Maryland Eastern Shore on a project focusing on the following goals:6
Evaluate barriers to AMS grant opportunities for socially disadvantaged communities
Invest in building trust and confidence between these communities and the USDA
Take action to rectify inequalities in program access through targeted outreach, training, and technical assistance.
The results of this work are intended to be used to improve access and reduce barriers for all applicants, presumably part of the agency’s renewed efforts to address USDA’s history of systemic discrimination.7
Community preparedness
Recent research suggests that the success of food system interventions, policies, and strategies for local economic development may hinge on the preexisting levels of community capital, as demonstrated by studies highlighting the pivotal role of social networks, trust, and local governance in achieving sustainable outcomes.8 9 10
Additional research showed positive associations between cultural and social capital and farm to school activity, indicating that communities with higher levels of social engagement and cultural assets are more likely to implement and sustain such programs successfully.11 12 13
Much of this research highlights community assets that are often overlooked in community development work, emphasizing the importance of recognizing and leveraging local strengths such as volunteer networks, informal social ties, and local knowledge systems for effective intervention.14 15 16
Objective
This report intends to lay the groundwork for an analytic approach that helps determine which community characteristics are associated with LAMP grant funding allocation. This could help determine if there is something akin to a “threshold of community preparedness” the unknowingly results in certain low-resource communities being excluded from LAMP programming. If so, the results of this research could provide insight into the particular characteristics associated with LAMP access, which could help agency staff to better allocate resources to ensure equitable access to grant funds.
Methods
Note
The code and data used to for the analysis and report generation can be accessed at https://github.com/elhohn/fas-capstone
Data access and aggregation
As a first step, a variety of potentially relevant data sets were obtained, cleaned, organized, and used for general data exploration. Information on specific datasets and sources can be found below. All work was done using the open source statistical software R version 4.4.0.17
LAMP grant data
Information on LAMP awards came from the LAMP Navigator website, where AMS has made this information publicly available, along with a dashboard for sorting, filtering, and visualizing the grant information.18 Along with information about the organizations receiving the grant, the available LAMP data includes information on the purpose of the grant (e.g., technical assistance, infrastructure, processing), the match amount, and the total project cost.
LAMP grant award amounts, 2006 - 2023
Each green dash represents a single grant award
For this analysis, the total award amount and the location of the organization receiving the grant were the key components used.
Geographic distribution of LAMP Grants, 2006-2023
Community characteristics
A variety of socioeconomic and environmental factors were investigated to assess how they may influence the likelihood of receiving a LAMP grant. These factors include indicators of community wealth, which encompasses social capital, natural capital, financial capital, and a variety of other forms of wealth, which have been shown impacts the ability to engage and participate in such programs.19 Additionally, it includes factors related to poverty and food security, which have been shown to exacerbate vulnerabilities and influence accessibility and participation in programs.20 Finally, considering the food system focus of LAMP, factors related to urbanization and proximity to agricultural land were included because they can influence market dynamics and food system connectivity.21
Indicators of community wealth
Community wealth data were accessed via the USDA AMS Data and Metrics GitHub repository.22 The main source of data was the “Indicators of Community Wealth” dataset stored within this repository, which is the output of various pre-processing steps that are outlined within the scripts found in the repository. Additional information about the variables used in this analysis can be found in the table below.
Table of indicators of community wealth variables and sources
| Description | Data Source | |
|---|---|---|
| Demographics | ||
| racial_div | Constructed racial diversity index from 0 (no diversity) to 10 (complete diversity), 2010 | U.S. Census Bureau, Modified Race Data (2010) |
| insured | Percent of population with health insurance | Robert Wood Johnson Foundation, County Health Rankings |
| health_factors | Health Factors Z-Score | Robert Wood Johnson Foundation, County Health Rankings |
| health_outcomes | Health Outcome Z-Score | Robert Wood Johnson Foundation, County Health Rankings |
| Labor | ||
| create_jobs | Percent of workforce employed in the arts | USDA Economic Research Service, Creative Class Codes |
| Institutions | ||
| ed_attain | Percent of adult population with at least a Bachelor's degree | U.S. Census Bureau, American Community Survey, table S1501 |
| Food Access | ||
| food_secure | Percent of population food secure | Feeding America Map the Meal Gap |
| Processing & Distribution | ||
| foodbev_est_CBP | Food and beverage manufacturing establishments per 10,000 people | U.S. Census Bureau, County Business Patterns |
| est_CBP | Other manufacturing establishments per 10,000 people | U.S. Census Bureau, County Business Patterns |
| Community Characteristics | ||
| highway_km | Inverse of population-weighted distance (km) to nearest interstate highway ramp | Dicken et al. (2011) |
| broad_16 | Percent of population with access to fixed advanced telecomm | FCC (2016) |
| pc1b_manufacturing | Constructed index derived from a prinicipal component analysis including food and beverage establishments, and other manufacturing establishments | Derived in Schmitt et al. (2021) |
| pc2b_infrastructure | Constructed index derived from a prinicipal component analysis including percent of population with access to telecommunications, and proximity to highway ramp | Derived in Schmitt et al. (2021) |
| create_indus | Creative industry businesses per 100,000 population, 2014 | Kushner & Cohen, Local Arts Index (2018) |
| pub_lib | Public libraries per 100,000 people | Kushner & Cohen, Local Arts Index (2018) |
| museums | Museums per 100,000 people | Kushner & Cohen, Local Arts Index (2018) |
| pc1c_artsdiversity | Constructed index derived from a prinicipal component analysis including percent of workforce employed in the arts, and racial diversity index | Derived in Schmitt et al. (2021) |
| pc2c_creativeindustries | Constructed index derived from a prinicipal component analysis including public libraries per 100,000 people, creative industry businesses per 100,000 people, and museums per 100,000 people. | Derived in Schmitt et al. (2021) |
| localgovfin | Cash and security holdings less government debt per capita | U.S. Census Bureau, Annual survey of state and local government finance. Historical data (formerly Special 60). File: “_IndFin_1967-2012” |
| owner_occupied | Owner-occupied units without a mortgage per capita | U.S. Census Bureau, American Community Survey, table S2507 |
| deposits | Bank deposits per capita at FDIC-insured institutions | FDIC, Deposit Market Share Reports - Summary of Deposits |
| pc1f | Financial capital - financial solvency | Derived in Schmitt et al. (2021) |
| primary_care | Number of primary care physicians per 10,000 population | Robert Wood Johnson Foundation, County Health Rankings |
| pc1h_healtheducation | Constructed index derived from a prinicipal component analysis of human capital data including educational attinment, health facor and outcome score from the Robert Wood Johnson Foundation | Derived in Schmitt et al. (2021) |
| pc2h_medicalfoodsecurity | Constructed index derived from a prinicipal component analysis of human capital data including percent of population food secure, percent of population with health insurance, and number of primary care phsyicans per 10,000. | Derived in Schmitt et al. (2021) |
| natamen_scale | Natural Amenities Scale | McGranahan, D., 1999. Natural Amenities Scale. U.S. Department of Agriculture, Economic Research Service |
| prime_farmland | Percent of farmland acres designated as prime farmland, 2012 | U.S. Department of Agriculture, Natural Resource Conservation Service (USDA NRCS). 2012. National Resources Inventory |
| conserve_acre | Percent of total acres with conservation easement, 2016 | National Conservation Easement Database (NCED), 2016. |
| acre_FSA | Percent of total acres in conservation-related programs and woodlands | U.S. Department of Agriculture, Farm Service Agency (USDA FSA). 2017. FSA Crop Acreage Data |
| acre_NFS | Percent of total acres in National Forests | U.S. Forest Service (USFS). 2017. Land areas of the National Forest System. FS-383. |
| pc1n_naturalamenitiesconservation | Constructed index derived from a prinicipal component analysis including natural amenity scale and share of acres in National Forest | Derived in Schmitt et al. (2021) |
| pc2n_farmland | Constructed index derived from a prinicipal component analysis including prime farmland | Derived in Schmitt et al. (2021) |
| pvote | Percent of eligible voters that voted | Rupasingha, Goetz, and Freshwater (2006) and 2017 data updates |
| nccs | Number of nonprofit organizations per 1,000 population | Rupasingha, Goetz, and Freshwater (2006) and 2017 data updates |
| assn | Number of social establishments per 1,000 population | Rupasingha, Goetz, and Freshwater (2006) and 2017 data updates |
| respn | U.S. Population Census response rate, percent | Rupasingha, Goetz, and Freshwater (2006) and 2017 data updates |
| pc1s_nonprofitsocialindustries | Constructed index derived from a prinicipal component analysis including number of social establishments and nonprofits per capita | Derived in Schmitt et al. (2021) |
| pc2s_publicvoiceparticipation | Constructed index derived from a prinicipal component analysis including public voice and participation | Derived in Schmitt et al. (2021) |
Distribution of community wealth metrics
For each category listed in the table above, all metrics within that category were normalized, then the mean of all normalized values in the category was used to create an categorical percentile score. The colors in each category map layer below correspond to this percentile score. The values for the individual metrics can be seen by clicking on the county polygons.